-- itt elég későn kezdtem el jegyzetelni --

Oszlopok kimutatása a majom látókérgében
	tracking visual pathway and detecting columns by injecting dye to thalamic cells
Szemidominancia ozslopok a majom és ember látókérgében
	human equivalent exists to monkey visual cortex pattern (yellow and blue patches)
Citokróm-oxidáz oszlopok...
	You cannot detect column-sensitive cells
	in 70's it turned out that there is an enzyme that aggregates in a bloby manner (citokróm-oxidáz oszlop=blob)
A Magno and Parvo pályák agykérgi kapcsolatai
	color is in blobs
	V1 is layer dependent, V2 is not, but columnar
	in V3 visual input is further processed to detect higher level objects
	dorsal: top of the brain (green in picture), ventral: sides of brain (pink)
	V4 is responsible for high level detection as well e.g. faces that are not turned to us directly
	
-- itt most sok kimaradt --

A látókéreg primer input alapvető tulajdonsága: antagonisztikus "center-surround" szerkezet
	receptive field type: it is divided to central and periferial areas
A receptív mező tulajdonságok drasztikusan változnak a látókéregben
	Experiment: start to sweep with a bar before the eye
		> Firing pattern in thalamus is independent from direction of the bar >> the receptive field is circularly symmetric
		> Firing pattern in visual cortex is highly dependent on orientatio

A "simple" típusú receptív mező kialakulásának elmélete az elsődleges látókéregben
	Orientation is extremely important
	Thalamo-cortical (TC) relay cells project to a "simple" cells
		> the on-center fields TC cells are aligned to each other along the directions they code together
		> the "simple" cell that is innervated by them are thus orientation-selective
A "complex" típusú erceptív mező kialakulása az elsődleges látókéregben
	...
A látókérdi receptív mezők szerkezete a Gábor funkció segítségével modellezhető
	A valóságban nincsenek éles határok
	In the receptive field there are also no sharp edges > it is rather a Gaussian distribution in firing pattern
	Gábor function is very simple, can be easily computed by computers, and it can be 100% controlled by parameters
	Other usage: construct, generate stimuli to investigate visual system
		> there is no noise or junk, the response is very clear for that stimuli
Simple Cell receptive fields: Spatial frequency diagram for with simplified stimuli
	First figure: small receptive fields does not response well to stimulus, but larger receptive fields does
	Second figure in first row: here small fields responds better
	Third row: phase if also effecting whether fields responds well or not
	Contrast and orientation also metters
Az orientáció szelektivitás kialakulásának mechanizmusa I:
FEED-FORWARD model
	there are debates whether the feed-forward (FF) or feed-back (FB) model is more descriptive
	Predikció: 
		align electrodes > projects output from one to other
		Subfield of thalamic cells correspond to input in case of 23 cells, but not for n=51
			> it is evidence of FF
		On the right: We measure thalamic input > it corresponds to receptive fields, but just in the direction of cells
	Kontraszt invariancia: Evidences against FF modell
		lower the constrast of signal > thalamic cells fires with less intensity
	Kontraszt invariancia: quantitative model
		A: preferred and non-preferred cells - does not change within cells
	...
	
Az orientáció szelektivitás kialakulásának mechanizmusa I:
FEED-BACK model
	injecting inhibition to inhibitory cells: cells loses their orientation-preference
	Alapvető sajátságok:
		general concept is: FB concept says that cortex processes information - FF concept states opposite
		intracortical mechanism > there is a difference between cortical inhibition and net cortical activity
			> narrow excitatory input
		the larger variety can be generated by inhibition, the narrower the excition will be

Az orientáció szelektivitás kialakításának alternatív modeljei:
	offset: mixture of models
	
Az agykéreg globális architektúrája:
	cortex is not uniform at all
	studying can be done by staining
Látókérgi idejsegtípusok osztályzása:
	Two big categories