Successfully reported this slideshow.
Upcoming SlideShare
×

# CCNExcercises:Neuron

210 views

Published on

Slides for 2nd Excercise in Computational Cognitive Neuroscience
dai.fmph.uniba.sk/courses/CCN/

Published in: Education
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

### CCNExcercises:Neuron

1. 1. Computational Cognitive Neuroscience Neuron Kristína Rebrová Kristína Rebrová Computational Cognitive NeuroscienceNeuron
2. 2. Neuron as detector Kristína Rebrová Computational Cognitive NeuroscienceNeuron
3. 3. Dynamics of Integration: tug-of-war Kristína Rebrová Computational Cognitive NeuroscienceNeuron
4. 4. Neurons variables gi : inhibitory conductance Ei : inhibitory driving potential Θ: action potential threshold Vm: membrane potential Ee: excitatory driving potential ge: excitatory conductance Ee: leak driving potential ge: leak conductance ¯g: maximum conductance Kristína Rebrová Computational Cognitive NeuroscienceNeuron
5. 5. 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
6. 6. Equilibrium Membrane Potential ge(t) = 1 n i xi wi xi = input to the neuron wi = weight of the neuron a weight deﬁnes how much unit listens to given input weights determine what the neuron detects = everything is encoded in weights Vm = ¯gege(t) ¯gege(t) + ¯gi gi (t) + ¯gl Ee + ¯gi gi (t) ¯gege(t) + ¯gi gi (t) + ¯gl Ei + ¯gl ¯gege(t) + ¯gi gi (t) + ¯gl El Kristína Rebrová Computational Cognitive NeuroscienceNeuron
7. 7. 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
8. 8. Rate Code Approximation to Spiking spikes to rates instantaneous and steady – smaller, faster models deﬁnitely lose several important things to ﬁnd an equation* that makes good approximation of actual spiking rate for same sets of inputs X-over-X-plus-1 (XX1) function Kristína Rebrová Computational Cognitive NeuroscienceNeuron
9. 9. The end Thank you for your attention kristina.rebrova@gmail.com Kristína Rebrová Computational Cognitive NeuroscienceNeuron