English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Optimization results are one method for understanding neural computation fromNature's perspective and for defining the physical limits on neuron-likeengineering. Earlier work looks at individual properties or performancecriteria and occasionally a combination of two, such as energy and information.Here we make use of Jaynes' maximum entropy method and combine a larger set ofconstraints, possibly dimensionally distinct, each expressible as anexpectation. The method identifies a likelihood-function and a sufficientstatistic arising from each such optimization. This likelihood is afirst-hitting time distribution in the exponential class. Particular constraintsets are identified that, from an optimal inference perspective, justifyearlier neurocomputational models. Interactions between constraints, mediatedthrough the inferred likelihood, restrict constraint-set parameterizations,e.g., the energy-budget limits estimation performance which, in turn, matchesan axonal communication constraint. Such linkages are, for biologists,experimental predictions of the method. In addition to the related likelihood,at least one type of constraint set implies marginal distributions, and in thiscase, a Shannon bits/joule statement arises.

Neural computation from first principles: Using the maximum entropy method to obtain an optimal bits-per-joule neuron