#summary Neural Connections Research
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== What are connections ==

  * connection is made between two neurons
   * connection is uni-directional
   * source of connection is always axon branch of first neuron
   * target of connection can be dendrite, soma or initial axon segment of second neuron
   * axon-axon connections are usually ignored in neural network models
  * axon-dendrite connection elements are:
   * neuron1 soma
   * neuron1 initial axon segment
   * neuron1 axon trunk (myelinated)
   * neuron1 axon branchX
   * neuron1 axon branchX terminal
   * inter-neuron space (synaptic cleft)
   * receptors of post-synaptic cell membrane on spine of neuron2 dendrite
   * neuron2 dendrite trunk
   * neuron2 soma
  * there are two types of synapses, electrical and chemical
   * *electrical synapse* - protein junction forms hole between axon terminal and post-synaptic neuron membranes, allowing the electrical signal to pass directly from one cell to another;
   * electrical synapse is much faster than chemical synapse, but unlike chemical synapse, cannot be regulated or controlled
   * *chemical synapses* may be regulated and are affected by methamphetamine, signals always travel from presynaptic membrane, through synaptic cleft, and to postsynaptic membrane

== Connection dynamics ==

  * connection can be stronger or weaker, thus having connectivity factor (see Jeff Hawkins)
  * above certain connectivity factor threshold connection allows signal propagation, when presynaptic signal (action potential) produces post-synaptic signal
  * below threshold connection still exists, because activity in both neurons affects connectivity factor
  * connectivity factor differs from connection weight of classical neural networks, as weight always produces output which depends on weight value; connectivity factor is continuous, but its effect is binary

== When connection is enforced ==

*Options under consideration*:
  * when signal propagates through connection
  * when action potential encounters fire state (Hebb's learning), e.g.:
   * just after firing there is negative potential in all dendrites
   * which electically attracts axon terminal having positive action potential
   * while firing is impossible (refractory period) and action potential energy is spent for increasing connectivity factor
  * using interneurons and non-neuron interneuron matter (glia)
  * using complex structures as controllers (bump attractors and so on)