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1

Actually it's a bit complicated but in simple terms : Neurotransmitters are stored in a synapse in synaptic vesicles, clustered beneath the membrane in the axon terminal located at the presynaptic side of the synapse. Neurotransmitters are released into and diffused across the synaptic cleft, where they bind to specific receptors in the membrane on the ...


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Two ideas on this so far: I think we have neurons representing multiple occurrences of a given feature, for example one neuron for "one face", one for "two faces", etc. At some number it doesn't really make a big difference anymore, so there is just a neuron for "group of faces". This would explain why we can recognize small number of objects in a glimpse ...


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While it's true that universal nouns will link mostly to other universal nouns (a tree is a plant), and universal verbs will link mostly to other universal verbs (to climb is to move), it is not necessary that they be in separate memory areas from each other; or even that they be separate from event memory (one cat did climb one tree). The most efficient ...


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yes, see de Almeida, et al., 2009 for a biologically plausible implementation http://www.jneurosci.org/content/29/23/7497.short


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I'm assuming that you want some kind of "computer vision" model (in that you want to be able to provide the model with input stimuli in the form of an image), and that you want to predict some kind of behaviour? (e.g., RT from a search task). Fleshing out the different processes involved is not going to be trivial, so there probably isn't a ...


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Given that all production rules reside in the Basal Ganglia and Thamalus, although it is not explicitly stated, the only place the rule compilation could feasibly take place is in the Basal Ganglia and Thalamus.


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Your guess 1 basically sounds like habituation: https://en.wikipedia.org/wiki/Habituation Per your clarification in your comment, 2 sounds like you are generally talking about the role of prediction error in learning. There's a lot of work on this. Neural network models generally learn by modifying the connection strengths in response to error. The most ...


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It appears that there is little scientific backing for Jung's theories. As these theories were first suggested at the start of the 20th century they have had a considerable amount of time to receive scientific support. If they were accepted by scientists it therefore seems like they would have been widely used and cited by scholars in the interim period. ...



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