03 Neurons as Computational Elements

• weighed inputs summed up to surpass a theshold resulting in an output

• adding extra layer (where it is computed?

• attraction through membrane

  —> acting like capacitor, keeping the charge

technically

single AP: does not reach threshold, smoothed charge/discharge

integration of APs

• specific nodes for abstract features adding up for face/animals nodes

• two ion gradients (more sodium (Na+) outside, more potassium inside (K+))

• negative potential inside the axon

• active NaKpump to keeo gradients stable

• voltage-gated channels for Na und K

(equlibrium potential: reason for +40mV, over/undershoot because of equilibrium)

• surpassing threshold —> Na channels open (Na influx), K channels slowly open (K outflux)

  —> depolarisation

• Na channels refractory

• K still open —> repolarisation

• extra K diffuses to restore membrane resting potential

• continually 1 m/s (smaller resistance when axon is thicker - invertebrates)

• saltatory up to 120 m/s with myelin sheets

• screenshot: electric conductor and water? why?

• dendrites

• attenuation

• 37%?

  
  

• AP -> Na+ flows to Point 2, depolarisation

• using voltage clamp on squid axon

• maths:

  • set of non-linear differential equations

  • ! taking opening probabilities into account

• It is concluded that the responses of an isolated giant axon of Loligo electrical stimuli are due to reversible alterations in sodium and potassium permeability arising from changes in membrane potential.

• frequency -> adaption/exhaustion

• shape and inputs of neurons

• reverse to normal ?

• just intracellular

Markram, 1994

Calcium transients in dendrites of neocortical neurons evoked by single subthreshold excitatory postsynaptic potentials via low-voltage-activated calcium channels.

https://www.pnas.org/doi/epdf/10.1073/pnas.91.11.5207

• not recording neurons’ ions but the flow (electrical field)

• switches depending on temporal and spatial distribution of other synapses

• boosts through voltage gated channels

• selective synapses with angle/orientation of layer -> neuron changes weight of input

• 
  

• temporal pattern recognition (Branco, 2010)

  • sensitive to direction and velocity

• used in lab

• not orthodromic

• firing when moving object of preffered orientation

• Thus, orientation-tuned neurons can compute their characteristic firing pattern by integrating spatially distributed synaptic inputs coding for multiple stimulus orientations.

  
  

https://pubmed.ncbi.nlm.nih.gov/20428163/