Bray-Curtis Metrics as Measure of Liquid State Machine Separation Ability in Function of Connections Density | Zendy

Grzegorz M. Wójcik | Zendy; Marcin Ważny | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Bray-Curtis Metrics as Measure of Liquid State Machine Separation Ability in Function of Connections Density

Author(s) -

Grzegorz M. Wójcik,

Marcin Ważny

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.05.490

Subject(s) - measure (data warehouse) , computer science , euclidean distance , euclidean geometry , function (biology) , separation (statistics) , state (computer science) , liquid state , artificial intelligence , algorithm , data mining , machine learning , mathematics , physics , geometry , evolutionary biology , biology , thermodynamics

eparation ability is one of two most important properties of Liquid State Machines used in the Liquid Computing theory. To measure the so-called distance of states that Liquid State Machine can exist in – different norms and metrics can be applied. Till now we have used the Euclidean distance to tell the distance of states representing different stimulations of simulated cortical microcircuits. In this paper we compare our previously used methods and the approach with Bray-Curtis measure of dissimilarity. Systematic analysis of efficiency and its comparison for a different number of simulated synapses present in the model will be discussed to some extent

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research