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I have one particular area to offer as a potential example. Keep in mind that likely no formalization of psychological concepts has heretofore been comprehensive (and the utility of a comprehensive "model" may be questionable, but I'll leave this for my more philosophical colleagues to consider). Reinforcement Learning I am not a psychologist, but from ...


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I myself have not come across a complete end-to-end hippocampal model, but I would imagine that such an implementation would be quite broad in scope. Edmund Rolls has some nice papers on the hippocampus, one of which is a particularly informative and recent review: Kesner RP, and Rolls ET. A computational theory of hippocampal function, and tests of the ...


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I've looked through that book briefly but never really got into it... if I could suggest some others, though: Dayan and Abbott's Theoretical Neuroscience is a standard text in computational neuroscience but if I understand your question correctly, you might enjoy Kevin Murphy's Machine Learning: A Probabilistic Perspective, or David MacKay's Information ...


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In the paper "Control of automated behavior: insights from the discrete sequence production task" a task similar to piano playing is discussed. The task, shown in the figure below, involves a user pressing a key corresponding to visual input. One of the most interesting results from the paper is, as shown below, after learning, the response times of ...


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Current is Voltage (driving potential) times conductance. As the membrane potential approaches the Nernst potential of the conductance, the current approaches 0. Conductances can be turned on or off through receptor binding, but there is no such thing as voltage-independent current injection (at least under physiologically relevant conditions). Indeed, ...


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By definition, current-based synapses are biologically implausible. After all, biological synapses are conductance-based [1]. It is possible that current based synapses are sufficient to answer the question you are asking. It is also possible that they are not. Generally, biological plausibility is not a matter of the parameter values you are using but ...


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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 ...


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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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