|WikiProject Neuroscience||(Rated C-class, High-importance)|
Delldot - Is the diagram here correct?
You state that the when both the center and the surround are dark, both the on center cell and the off center cell do not fire. If I'm understanding receptive fields correctly (which is a pretty damn hard thing to do), I would think the off center cell would fire under these conditions for two reasons:
1. A photoreceptor can either depolarized or hyperpolarized (release or not release glutamate), which leads to opposite reactions in ON and OFF cells [(glutamate --> ON cell inhibited / OFF cell excited) OR (no glutamate --> ON cell excited / OFF cell inhibited)]. So if the ON cell is inhibited in the dark center/surround scenario, wouldn't the OFF cell be excited?
2. The OFF cell is naturally excited by dark current, and there is nothing to inhibit this response
A. The OFF cell's photoreceptor is depolarized in the dark --> PR releaes glutamate --> OFF cell is excited (depolarized) B. Horizontal cells from adjacent photoreceptors are also depolarized --> H1 cells are depolarized --> PR synaptic cleft is depolarized --> does not affect the release of glutamate from PR (since PR depolarization is already causing neurotransmitter to be released) --> OFF cell remains excited
I'm confused about the two definitions in the first two paragraphs. In most cases they don't refer to the same thing, right?
- The second paragraph is poorly worded. It ought to say that the receptive field of a higher order neuron is the combination of the receptive fields of the neurons that project to it, or something like that. It wouldn't be strictly true stated in that way, but probably comes somewhere into the neighborhood. Looie496 (talk) 03:33, 25 January 2009 (UTC)
- I agree about the second paragraph being poorly worded. I'm confused why the author calls the process of ganglionic cells being receptive fields for higher-order neurons "convergence", when the linked page on "convergence" refers to a mechanical effort by the muscles of the eyes to maintain binocular vision. Anyone have any idea if there's a different page the author had in mind, or if the process is even formally called "convergence" at all? Falquaddoomi (talk) 04:03, 6 May 2010 (UTC)
I suspect that the author means "amplitude" instead of "volume". If so, someone please fix it. In perception, it is more than possible that volume may actually mean volume (i.e. distance^3). —Preceding unsigned comment added by Sjgooch (talk • contribs) 16:30, 18 February 2010 (UTC)
Perhaps the importance of the concepts of receptive fields to understanding how the brain perceives the world as a unitary whole should be included along with links to related and opposing theories to how the brain accomplishes this unity. Two alternative explanations for the integration of inputs in a complex visual scene (for example) come to mind: the theory that this is accomplished through the collective properties of arrays of neurons and the theory that this is accomplished at higher level processing. Neuro Sci Student (talk) 03:21, 25 January 2009 (UTC)
- Could you be a bit more specific about what you think the article ought to say? Looie496 (talk) 03:28, 25 January 2009 (UTC)
Diagram has a mistake?
In ON ganglion cells, if the centre has light, and surround has light, the response will be weakened. If they are both in the dark, there will be no response.
In your diagram, you show that it is the same case for the OFF ganglion cells, but shouldnt it be the other way around, ie: if centre has light and surround has light, there will be no response, but if they both are in the dark, the response will be weakened?
The "Auditory system" section presently says:
"In the auditory system, receptive fields can correspond to volumes in auditory space, or to regions of auditory frequencies. Researchers rarely equate auditory receptive fields to particular regions of the sensory epithelium such as, in the case of mammals, hair cells in the cochlea."
I'm not an expert and was wondering if an expert could assess those claims. They are not supported by a reliable source. Actually, most of the article is missing sources. Is there an expert in the room who would be willing to give a brief review of the article and highlight anything dubious? It would be comforting to know, to begin with, that there are no obvious falsehoods. --Anthonyhcole (talk · contribs · email) 11:33, 7 June 2014 (UTC)
- I did ask a neuroscientist here (at the Royal Society workshop) and unfortunately it is not really his field. I will ask him again when he is free to have a look over the article and see if he spots anything dubious. I agree that the article needs attention and better/more sources. Carcharoth (talk) 11:41, 7 June 2014 (UTC)
- A glance at my "nerve" illustration from people who know what they're talking about would be very much appreciated too, if it seems appropriate to suggest it at any point. The best place to leave a comment on the illustration would probably be Talk:Nerve#Proposed illustration. --Anthonyhcole (talk · contribs · email) 11:55, 7 June 2014 (UTC))
I'm not sure what Deep learning has to do with anything regarding receptive fields. Center surround and receptive field research has inspired many, very different, approaches; deep learning may be one of them. That does not mean we should highlight it because it's the hot thing of the day.
What say the community : to remove or not remove?
- On the one hand convolutional neural networks are born out of the receptive field analogy. On the other, there are many more deep learning networks than there are receptive field types. Maybe it's worth mentioning in the introduction that the receptive field has inspired machine learning techniques, and then link to some machine learning page.MNegrello (talk) 17:27, 29 April 2016 (UTC)