# Tag Info

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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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The Hodgkin and Huxley model of neuronal firing is based on non-linear differential equations. A significant portion of research on sensation and perception is based on such models.

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Check out all of the videos in this playlist https://www.youtube.com/watch?v=lrppe54fixc&list=PL1hKzFfV5qJlVTjD8XWzyiaHxBZZ7iWs9&index=3 The particular video I linked gives the example of the "Papez circuit". Other interviewees mention other examples as well.

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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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Not intuitive, but to save the value of a curve plotted in a graph: Right click the graph window, and choose "Pick Vector" Click on the desired curve (it should change color i.e. to red) In the NEURON Main Menu > Vector > Save to File Type in the file name > Save File will have two columns, first one for X-axis values (i.e. time) and second for the Y-axis (...

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A large and notable area within psychology to which differential equations are central is dynamical systems theory. It is widely applied to cognitive science. Differential equations are used to describe (model) the states on one or more dimensions that a system can take which changes over time. Dynamical systems have been widely used to model cognition. ...

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According to this research paper by Lockary and Goodman, the resting membrane potential of C.Elegans is two fold because its a stepped resting potential of -70mV and -35mV. After an initial activation a resting potential of -35mV is held following a higher depolarisation, so on your graph the initial depolarisation would be a little higher, and then drops to ...

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ρ(t) in Equation 1.1 cannot be summed and averaged because the idealized spikes are infinitesimally narrow and the chance of two spikes from different trials occurring at the same time t is zero. In Equation 1.2, the idea of convolving each idealized spike with a "well behaved function" h(t). If you use a rectangular function around zero as h(t), with a ...

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This isn't exactly what you are referring to, but I think provides a similar function and has been shown in vertebrate vision: https://en.wikipedia.org/wiki/Normalization_model Divisive normalization as a canonical computation across the brain While this does not implement histogram equalization, I think it is actually a better-suited explanation of the ...

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A repository of publicly available NEURON models can be found on ModelDB by filtering for Models that contain the Modeling Application: NEURON.

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The weights in an artificial neural network are an approximation of multiple processes combined that take place in biological neurons. Myelination plays a role, but not a major one. Weights in artificial neural networks can be positive or negative numbers. Weight magnitude. The magnitude of a weight is analogous to a combination of increased dendritic ...

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The weight matrix is typically considered to be a strength of connectivity metric between nodes in the parallel computronium model that neural networks are based on. That fact is fairly evident when you investigate ANN learning algorithms. For instance, the backpropagation algorithm for feedforward networks is designed to strengthen a string of associations ...

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Short answer Mostly Cl- is disregarded in calculations of the resting membrane potential and action potential voltage changes, because it is less important for the neural membrane characteristics than Na+ and K+. Background In some neurons Cl- is not actively transported. In terms of the resting membrane potential, Cl- hence settles its gradient passively ...

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In normal neurons, Chloride's reversal potential is near the resting potential for the neuron and also happens to be near the leak conductance reversal potential for the neuron. While not exactly the same these three are sometimes confused. The difference between these three reversal potentials is subtle. Chloride Reversal Potential: is the potential ...

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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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Typing the following into the NEURON console will reset and run the simulation: run()

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If anyone ever finds this and has the same problem: The original paper has a flaw in the paper: alphac=(exp((Vd/mV-10)/11)-exp((Vd/mV-6.5)/27))/(18.975)* (Vd<=50*mV)/ms+2*exp((6.5-Vd/mV)/27)*(Vd>50*mV)/ms : Hz An errata published later changes this to: alphac=(exp((Vd/mV-10)/11-(Vd/mV-6.5)/27)/18.975)*(Vd<=50*mV)/ms+2*exp((6.5-Vd/mV)/27)*(Vd>...

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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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What they mean is that as long as you rotate $C_i$ and $C_j$ by the same amount, the dot product will be the same. This is rotational invariance because the dot product is invariant to coherent rotation of the relevant vectors. In neural terms, the correlation between the activity of two neurons (in a population representing a one dimensional circular ...

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The oscillations measured by EEG, ECoG, and MEG are thought to originate in the apical dendrites' fluctuating potentials. Try looking into the forward problem of EEG. Like the inverse problem but in reverse. Given a known source potential (apparently it's very common to model cortical neurons' LFP as a distribution of current dipoles?) you solve the Poisson ...

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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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This is actual a pretty old and often debated question. It is called "Lady Lovelace's Objection" and first appeared in Alan Turing's seminal paper "Computing Machinery and Intelligence". Below is my response to the objection, as well as Alan Turing's response which I wrote for a philosophy course about a year ago. Perhaps it will be of interest to you? ...

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Because of variation between organisms, cells in the same organism, or even the same cell separated by a few days, can have different parameter set. However, I think this is where parameter fitting becomes useful. As suggested in the comments knowing how parameters change between cells or over time can be very insightful. Parameter Estimation is very ...

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You really want to read the work by Gyorgy Buzsaki. The recent Schomburg et al., 2014 is quite illuminating although quite dense. Try my F1000 review for starters. http://f1000.com/prime/contributor/evaluate/article/718892254 The original paper can be found here http://www.sciencedirect.com/science/article/pii/S0896627314007818

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i once wrote a paper doing biophysical modeling of neurons that could create plateau potentials. While the paper itself is not exactly what you are looking for, there should be lots of good references in there: http://www.jneurosci.org/content/33/2/424.short Sanders H, Berends M, Major G, Goldman MS, Lisman JE. (2013) NMDA and GABAB (KIR) Conductances: ...

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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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A paper just came out last week in Neuron demonstrating dendrite-specific gating: http://www.cell.com/neuron/fulltext/S0896-6273(16)00054-4?elsca1=etoc&elsca2=email&elsca3=0896-6273_20160302_89_5_&elsca4=Cell%20Press Structured Dendritic Inhibition Supports Branch-Selective Integration in CA1 Pyramidal Cells Erik B. Bloss, Mark S. Cembrowski, ...

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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 "one-size-fits-...

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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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