Hodgkin–Huxley model

Target entity: Hodgkin–Huxley model
Context triple: [Alan Hodgkin, knownFor, Hodgkin–Huxley model]

• A. all-or-none principle in nerve excitation
  The all-or-none principle in nerve excitation is the physiological rule that a nerve fiber, once stimulated beyond a certain threshold, responds with a full, uniform action potential rather than a graded response.
• B. SNN
  SNN is the National Rail station code assigned to Swinton railway station in South Yorkshire, England.
• C. Ian Hodgkin
  Ian Hodgkin is a person notable enough to be recognized as a bearer of the Hodgkin surname.
• D. Hebbian learning
  Hebbian learning is a neurobiological and computational learning principle often summarized as "cells that fire together wire together," where the connection between neurons is strengthened when they are activated simultaneously.
• E. Hopfield networks
  Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: Hodgkin–Huxley model
Target entity description: The Hodgkin–Huxley model is a mathematical description of how action potentials in neurons are initiated and propagated through voltage-gated ion channels in the cell membrane.

• A. all-or-none principle in nerve excitation
  The all-or-none principle in nerve excitation is the physiological rule that a nerve fiber, once stimulated beyond a certain threshold, responds with a full, uniform action potential rather than a graded response.
• B. SNN
  SNN is the National Rail station code assigned to Swinton railway station in South Yorkshire, England.
• C. Ian Hodgkin
  Ian Hodgkin is a person notable enough to be recognized as a bearer of the Hodgkin surname.
• D. Hebbian learning
  Hebbian learning is a neurobiological and computational learning principle often summarized as "cells that fire together wire together," where the connection between neurons is strengthened when they are activated simultaneously.
• E. Hopfield networks
  Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
• F. None of above. chosen