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8

Read dayan and abbot "theoretical neuroscience" Learn differential equations Know the relationship between voltage, current, resistance and conductance Differential equations is absolutely essential though. you don't need to learn to solve them (the computer will do that for you), you just need to learn to know what they mean. How do researchers ...


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ACT-R can best be summarized with this (tiny but more recent) graph: ACT-R is a cognitive architecture that tries to explain as much of human behavior as possible with as little rules as possible. It works at a high level of abstraction and came down to a list of so-called "modules", each having its own functions. The exact mechanisms of each of these ...


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In general what you're looking for is a biologically plausible model of reinforcement learning and/or conditioning. I know of two publications in particular that address this. The first is A Biologically Plausible Spiking Neuron Model of Fear Conditioning and the second is A Spiking Neural Integrator Model of the Adaptive Control of Action by the Medial ...


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I would classify pavlovian learning as a type of hebbian learning. Where events that occur together positively reinforce each other (different from reinforcement learning). This idea has been modified into hopfield networks, and then their descendants boltzmann and restricted boltzmann machines. They use an algorithm called contrastive divergence which is ...


5

My answer is probably a weird hodgepodge of sometimes poorly explained stuff, but hopefully it's coherent enough :P For many decades in psychology, we've had a mechanistic stimulus-organism-response understanding of the brain. That is, a stimulus triggers an internal psychological process, which produces some behavioral response. One of the major ...


4

I'm a TMS, but not an fMRI, researcher but I have experience using anatomical masks. I can guide you through the steps to make an anatomical mask, but unfortunately I don't have any code to offer you. First of all you need a set of x, y and z co-ordinates that specify where TOFC or LOC are. One way to find these is to find a paper that reports the x,y,z ...


4

This article explains the difference : Declarative memory takes the form of a semantic net linking propositions, images, and sequences by associations. The nodes of long-term memory all have some degree of activation and working memory is that part of long-term memory that is most highly activated. The declarative memory is all knowledge ...


4

I found two papers in the same vein with considerably more empirical evidence. The first paper is Modeling the Size of Wars. In the paper, provinces and conflicts are modeled to justify the Richardson's observation that the proportion of the severity of conflicts in relation to their frequency is described by a power law. In other words, the more space ...


3

A good introductory book is "Networks of the Brain" by Olaf Sporns. It will give you a general overview of the ideas and theories in the field. For more details consult the bibliography or read some of the journal articles by Dr. Sporns. https://mitpress.mit.edu/books/networks-brain http://www.indiana.edu/~cortex/index.html


3

I agree with the previous answer/comments that seeking a simplified abstract model of the brain when it is so complex is probably asking too much. We would need to know a lot more about the "states" you are talking about in order to model them, and in reality the set of "triggers" etc is going to be far too long. However, given your interest and analogy ...


3

This question cannot be answered in the form in which you asked it both because of the limits of current neuroscientific theories and methodologies when it comes to determining the structures of complex neural representations (although we have made headway in a few cases such as place cells and grid cells), and because neural representations are not really ...


3

I totally agree. These words have become colloquial and have lost much of their meaning - and are therefore increasingly less helpful. Introversion is confused with shyness and sensitivity, while Extroversion is confused with social skills. This is partly because there is no agreed upon definition, insofar that they are operationally defined by each ...


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Most flow diagrams with the detail your require are for rudimentary sensory functions (such as seeing, eye tracking and other simple functions) can be found in any neuroanatomy textbook. The one I have experience with is "Neuroanatomy: Text and Atlas" by John H. Martin. Alternatively, if you're looking for a more functional interpretation of how information ...


2

This question seems to be asking "How is knowledge represented in neurons" and then jumps to the assumption "synapses represents relations". As discussed in the linked question, there is a lot of evidence that this method of binding is not biologically plausible, given that it doesn't scale well to the level of human vocabulary. So, to answer your question, ...


2

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. ...


2

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 ...


1

Your question reduces to "what are the neural mechanisms behind language?", which is very much a work in progress. The only neural model of language that I currently know of is the Semantic Pointer Architecture (SPA), which is largely theoretical and only has some super basic examples. Basically, the SPA represents language as vector manipulation. The ...


1

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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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 ...


1

You are basically asking how to bind different concepts together based off their representation in neurons. The one way I know how to do this is using the Semantic Pointer Architecture (SPA). To understand how lower level neural activities can be compressed as a concept, please see my answer on compression in the brain using the SPA. This answer explains ...


1

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 ...


1

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 "one-size-fits-...


1

ACT-R is a complete cognitive model that incorporates Working Memory, Declarative Memory and Procedural Memory, but also incorporates input (visual and auditory) and output (manual) buffers. It is a really interesting model about the human in its entirety but, it is at a high level of abstraction. The paper about memory you want is "REFLECTIONS OF THE ...


1

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 ...


1

A true classic -- the Configural-Cue model -- uses the Rescorla-Wagner rule to learn associations between cues and outcomes. Link1 Link2 Link3 In my view this is one of the most straightforward (i.e., simplest) models of conditioning, likely a good starting point for you.


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It is generally thought that thalamic input comes in layer 4, feed back from higher areas come through layer 1 to layer 2/3 and feed forward is sent from the deeper layers. see Canonical Microcircuits for Predictive Coding Andre Bastos, W. Martin Usrey, Rick Adams, George Mangun, Pascal Fries, and Karl Friston http://psych.nyu.edu/clash/poeppellab/...


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Unless I'm misunderstanding what you mean, ratio of evidence is actually a terrible method because that would end up with an immediate decision as soon as any evidence is encountered for either side, giving a ratio of infinity for that side (something/0 is greater than any finite decision threshold). Try Gold and Shadlen 2007 for a review http://synapse....


1

After having thought more carefully about my own question, I give my own answer. I would say that in the quote: The literature shows: (1) knowing that one’s partner has defected leads to a higher probability of defection; (2) knowing that one’s partner has cooperated also leads to a higher probability of defection; and, most troubling for ...


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We do not know a lot about how dreams work yet. However, there are some theories out there. One theory of particular interest is that are dreams are a means of reverse learning. In 1983 Francis Crick of the Salk Institute in La Jolla, Calif., and Graeme Mitchison of the University of Cambridge in England proposed the idea of reverse learning. ...



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