Figure_2.tif (166.14 kB)
Attractor states of a two-input AHaH node.
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posted on 2014-02-10, 03:23 authored by Michael Alexander Nugent, Timothy Wesley MolterThe AHaH rule naturally forms decision boundaries that maximize the margin between data distributions (black blobs). This is easily visualized in two dimensions, but it is equally valid for any number of inputs. Attractor states are represented by decision boundaries A, B, C (green dotted lines) and D (red dashed line). Each state has a corresponding anti-state: . State A is the null state and its occupation is inhibited by the bias. State D has not yet been reliably achieved in circuit simulations.
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Computational biologycomputational neuroscienceCircuit modelsCoding mechanismsneuroscienceCognitive neurosciencecognitionDecision makingMotor reactionsSensory systemsVisual systemLearning and memoryMotor systemsneural networksalgorithmsComputer architectureComputer hardwareComputing systemsHybrid computingtext miningElectrical engineeringElectronics engineeringSolid state physicsstatestwo-inputahah
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