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Brain research up to date has revealed that structure and function are highly related. Thus, for example, studies have repeatedly shown that the brains of patients suffering from schizophrenia or other diseases have a different connectome compared to healthy people. Apart from stochastic processes, however, an inherent logic describing how neurons connect to each other has not yet been identified. We revisited this structural dilemma by comparing and analyzing artificial and biological-based neural networks. Namely, we used feed-forward and recurrent artificial neural networks as well as networks based on the structure of the micro-connectome of C. elegans and of the human macro-connectome. We trained these diverse networks, which markedly differ in their architecture, initialization and pruning technique, and we found remarkable parallels between biological-based and artificial neural networks, as we were additionally able to show that the dilemma is also present in artificial neural networks. Our findings show that structure contains all the information, but that this structure is not exclusive. Indeed, the same structure was able to solve completely different problems with only minimal adjustments. We particularly put interest on the influence of weights and the neuron offset value, as they show a different adaption behaviour. Our findings open up new questions in the fields of artificial and biological information processing research.
Pubmed ID: 33692408
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Wolfram Alpha is a computational knowledge engine that to make all systematic knowledge computable. Although, this is not a specific neuroscience related resource, the computational and math element will be helpful. Wolfram Alpha's long-term goal is to make all systematic knowledge immediately computable and accessible to everyone. It aims to collect and curate all objective data; implement every known model, method, and algorithm; and make it possible to compute whatever can be computed about anything. It's goal is to build on the achievements of science and other systematizations of knowledge to provide a single source that can be relied on by everyone for definitive answers to factual queries. Wolfram Alpha aims to also bring expert-level knowledge and capabilities to the broadest possible range of peoplespanning all professions and education levels. It's goal is to accept completely free-form input, and to serve as a knowledge engine that generates powerful results and presents them with maximum clarity. Lastly, Wolfram Alpha is an ambitious, long-term intellectual endeavor that it intends to deliver increasing capabilities over the years and decades to come. With a world-class team and participation from top outside experts in countless fields, it's goal is to create something that will stand as a major milestone of 21st century intellectual achievement. As of now, Wolfram Alpha contains 10+ trillion pieces of data, 50,000+ types of algorithms and models, and linguistic capabilities for 1000+ domains. Built with Mathematicawhich is itself the result of more than 20 years of development at Wolfram ResearchWolfram Alpha's core code base now exceeds 5 million lines of symbolic Mathematica code. Running on supercomputer-class compute clusters, Wolfram Alpha makes extensive use of the latest generation of web and parallel computing technologies, including webMathematica and gridMathematica. Its knowledge base and capabilities already span a great many domains, and its underlying framework has the power and flexibility to support ready extension to essentially any domain that is based on systematic knowledge.
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