Difference between revisions of "NeuronConnectionsResearch"

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Latest revision as of 19:08, 28 November 2018

Neural Connections Research

@@Home -> NeuralNetworksResearch -> NeuronConnectionsResearch


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)