10.1371/journal.pone.0085175.g002
Michael Alexander Nugent
Michael
Alexander Nugent
Timothy Wesley Molter
Timothy
Wesley Molter
Attractor states of a two-input AHaH node.
Public Library of Science
2014
Computational biology
computational neuroscience
Circuit models
Coding mechanisms
neuroscience
Cognitive neuroscience
cognition
Decision making
Motor reactions
Sensory systems
Visual system
Learning and memory
Motor systems
neural networks
algorithms
Computer architecture
Computer hardware
Computing systems
Hybrid computing
text mining
Electrical engineering
Electronics engineering
Solid state physics
states
two-input
ahah
2014-02-10 03:23:44
Figure
https://plos.figshare.com/articles/figure/_Attractor_states_of_a_two_input_AHaH_node_/929347
<p>The 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.</p>