Difference between revisions of "NeuronConnectionsResearch"

Latest revision as of 19:08, 28 November 2018

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)