CCNExcercises:Neuron
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Transcript of CCNExcercises:Neuron
![Page 1: CCNExcercises:Neuron](https://reader035.fdocument.pub/reader035/viewer/2022071815/55a96acb1a28ab516e8b47ea/html5/thumbnails/1.jpg)
Computational Cognitive NeuroscienceNeuron
Kristína Rebrová
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
![Page 2: CCNExcercises:Neuron](https://reader035.fdocument.pub/reader035/viewer/2022071815/55a96acb1a28ab516e8b47ea/html5/thumbnails/2.jpg)
Neuron as detector
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
![Page 3: CCNExcercises:Neuron](https://reader035.fdocument.pub/reader035/viewer/2022071815/55a96acb1a28ab516e8b47ea/html5/thumbnails/3.jpg)
Dynamics of Integration: tug-of-war
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
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Neurons variables
gi : inhibitory conductanceEi : inhibitory driving potentialΘ: action potential thresholdVm: membrane potentialEe : excitatory driving potentialge : excitatory conductanceEe : leak driving potentialge : leak conductanceg : maximum conductance
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
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Neural integration
Vm(t) = Vm(t−1)+dtvm[ge(Ee−Vm)+gi (Ei−Vm)+gl (El−Vm)]
Ie = ge(Ee − Vm)
Ii = gi (Ei − Vm)
Il = gl (El − Vm)
Inet = Ie + Ii + Il
Vm(t) = Vm(t − 1) + dtvmInet
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
![Page 6: CCNExcercises:Neuron](https://reader035.fdocument.pub/reader035/viewer/2022071815/55a96acb1a28ab516e8b47ea/html5/thumbnails/6.jpg)
Equilibrium Membrane Potential
ge(t) =1n
∑i
xiwi
xi = input to the neuronwi = weight of the neurona weight defines how much unit listens to given inputweights determine what the neuron detects = everything isencoded in weights
Vm =gege(t)
gege(t) + gigi (t) + glEe +
gigi (t)
gege(t) + gigi (t) + glEi+
gl
gege(t) + gigi (t) + glEl
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
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Generating Output
If Vm gets over threshold, neuron fires a spike.Spike resets membrane potential back to rest.Vm has to climb back up to threshold to spike again
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
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Rate Code Approximation to Spiking
spikes to ratesinstantaneous and steady – smaller, faster modelsdefinitely lose several important thingsto find an equation* that makes good approximation of actualspiking rate for same sets of inputsX-over-X-plus-1 (XX1) function
Kristína Rebrová Computational Cognitive NeuroscienceNeuron
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The end
Thank you for your attention
Kristína Rebrová Computational Cognitive NeuroscienceNeuron