"... First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. ..."
Model Type: Neuron or other electrically excitable cell; Realistic Network
Region(s) or Organism(s): Neocortex
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Neocortex L5/6 pyramidal GLU cell; Neocortex layer 4 pyramidal cell; Neocortex spiny stellate cell
Model Concept(s): Action Potentials; Active Dendrites; Simplified Models
Simulation Environment: Python (web link to model)
Implementer(s): Ahmad, Subutai [sahmad at numenta.com]
References:
Hawkins J, Ahmad S. (2016). Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Frontiers in neural circuits. 10 [PubMed]