Introduction to Computational Neuroscience P1

Question 1

what is a descriptive (statistical) model

Answer 1

quantify magnitude, timing or location of neural activity

Question 2

limitation of descriptive model

Answer 2

does not explain mechanism behind patterns

Question 3

generative model

Answer 3

input (visual/auditory) processing(neural/computation) output (neural activity/behaviour)

intervention targets processing

Question 4

benefits of generative model

Answer 4

explains the mechanism behind the behaviour

provides us ideas of treatment in neurological/psychiatric conditions

Question 5

descriptive model of action potentials

Answer 5

shows likelihood of firing
timing of peak
no of AP per second

does not tell why AP occurs

Question 6

hogdkin-huxley model

Answer 6

provides mechanistic model of the AP
formed using differential equations (models rate of change of voltage)

Question 7

what occurs if voltage is removed from AP

Answer 7

slight depolarisation but no AP

Question 8

define parameter

Answer 8

a value that influences how a model behaves

Question 9

example hogdkin huxley parameters

Answer 9

how quickly potassium channels open and close

Question 10

use of iPSCs

Answer 10

obtain iPSCs from patient with neurological condition to derive neurons

apply constant voltage to neurons, record AP
see atypical pattern
identify treatment

Question 11

limitations of Hogdkin-Huxley model

Answer 11

computational models are abstract and simple
more factors influence the firing of neurons